TNSNet: Thyroid nodule segmentation in ultrasound imaging using soft shape supervision

Sun, Jiawei; Li, Chunying; Lu, Zhengda; He, Mu; Zhao, Tong; Li, Xiaoqin; Gao, Liugang; Xie, Kai; Lin, Tao; Sui, Jian-feng; Qin, Xi; Zhang, Fan; Ni, Xinye

doi:10.1016/j.cmpb.2021.106600

Cited by 23 publications

(10 citation statements)

References 19 publications

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“…[11] Multiple past works have developed novel deep learning architectures to address automated segmentation of thyroid nodules. [12; 13; 14; 15] A majority of these works have studied the U-Net architecture; however, this architecture uses the same convolutional filter size, resulting in a fixed receptive field which hampers the segmentation of objects that vary in size. In response to this issue, Su et al proposed MSUNet,[16] which introduces a multi-scale block in each layer of the encoder to fuse the outputs of convolution kernels with different receptive fields.…”

Section: Introductionmentioning

confidence: 99%

“…[11] Multiple groups have explored different deep learning architectures to address localization through the segmentation of thyroid nodules. [12]- [15] While these existing models demonstrate promising results, they are semi-automated because they are evaluated solely on images known to have nodules and do not assess ability to identify suspicious images. Thus, most current work tackles a more narrow task, which despite performing well, does not translate in terms of clinical applicability.…”

Section: Introductionmentioning

confidence: 99%

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A Multitask Approach for Automated Detection and Segmentation of Thyroid Nodules in Ultrasound Images

Radhachandran

Kinzel

Chen

et al. 2023

Preprint

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Objective: Poor risk stratification of thyroid nodules by ultrasound has motivated the need for a deep learning-based approach for nodule segmentation. This paper demonstrates the effectiveness of a multitask approach to detect ultrasounds containing potential nodules and segment nodules on the suspected images. Methods: Ultrasound studies were acquired from 280 patients at UCLA Health, totaling 9888 images. A novel anomaly detection (AD) module, to classify suspicious ultrasound images, was integrated with various state of the art segmentation architectures. The trained models were evaluated on a portion of the in-house dataset, as well as two external validation (EV) sets, to understand how the AD module affected segmentation performance. Results: The addition of AD to the architectures improved image-level nodule detection, evidenced by the increase in F1 scores and image-wide Dice similarity coefficient. Of the models with AD, MSUNet-AD had the highest F1 score of 0.829; however, there was a decrease in DSC on just images with nodules (DSC+) from 0.726 to 0.627. This drop was observed across all models when AD was added; however, closer analysis of DSC+by nodule size revealed that this difference was not significant in larger nodules, which are more likely to be clinically relevant. Finally, evaluation of MSUNet with and without AD on the EV datasets demonstrated comparable performance with the UCLA dataset. Conclusion: The proposed architecture is an automated multitask method that can both detect and segment nodules in ultrasound. Performance on the EV datasets demonstrates generalizability of the model.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Multitask Approach for Automated Detection and Segmentation of Thyroid Nodules in Ultrasound Images

Radhachandran

Kinzel

Chen

et al. 2023

Preprint

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“…On the one hand, the application of objective and stable, easy to operate, high accuracy CAD software helps to speed up the diagnosis and treatment process of US doctors and shorten the waiting time; on the other hand, it improves the accuracy and consistency of TI-RADS classification and avoids excessive fine-needle aspiration (FNA) caused by subjective factors and diagnostic techniques. Since the initial reporting of the diagnostic performance of the CAD system for thyroid lesions [ 15 ], several studies have shown that CAD methods have improved the diagnostic performance of thyroid US [ 16 , 17 ].…”

Section: Introductionmentioning

confidence: 99%

Accuracy of Ultrasound Diagnosis of Thyroid Nodules Based on Artificial Intelligence-Assisted Diagnostic Technology: A Systematic Review and Meta-Analysis

Xue

Zhou

Wang

et al. 2022

International Journal of Endocrinology

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Background. Ultrasonography (US) is the most common method of identifying thyroid nodules, but US images require an experienced surgeon for identification. Many artificial intelligence (AI) techniques such as computer-aided diagnostic systems (CAD), deep learning (DL), and machine learning (ML) have been used to assist in the diagnosis of thyroid nodules, but whether AI techniques can improve the diagnostic accuracy of thyroid nodules still needs to be explored. Objective. To clarify the accuracy of AI-based thyroid nodule US images for differentiating benign and malignant thyroid nodules. Methods. A search strategy of “subject terms + key words” was used to search PubMed, Cochrane Library, Embase, Web of Science, China Biology Medicine (CBM), and China National Knowledge Infrastructure (CNKI) for studies on AI-assisted diagnosis of thyroid nodules based on US images. The summarized receiver operating characteristic (SROC) curve and the pooled sensitivity and specificity were used to assess the performance of the diagnostic tests. The quality assessment of diagnostics accuracy studies-2 (QUADAS-2) tool was used to assess the methodological quality of the included studies. The Review Manager 5.3 and Stata 15 were used to process the data. Subgroup analysis was based on the integrity of data collection. Results. A total of 25 studies with 17,429 US images of thyroid nodules were included. AI-assisted diagnostic techniques had better diagnostic efficacy in the diagnosis of benign and malignant thyroid nodules: sensitivity 0.88 (95% CI: (0.85–0.90)), specificity 0.81 (95% CI: 0.74–0.86), diagnostic odds ratio (DOR) 30 (95% CI: 19–46). The SROC curve indicated that the area under the curve (AUC) was 0.92 (95% CI: 0.89–0.94). Threshold effect analysis showed a Spearman correlation coefficient: 0.17 < 0.5, suggesting no threshold effect for the included studies. After a meta-regression analysis of 4 different subgroups, the results showed a statistically significant effect of mean age ≥50 years on heterogeneity. Compared with studies with an average age of ≥50 years, AI-assisted diagnostic techniques had higher diagnostic performance in studies with an average age of <50 years (0.89 (95% CI: 0.87–0.92) vs. 0.80 (95% CI: 0.73–0.88)), (0.83 (95% CI: 0.77–0.88) vs. 0.73 (95% CI: 0.60–0.87)). Conclusions. AI-assisted diagnostic techniques had good diagnostic efficacy for thyroid nodules. For the diagnosis of <50 year olds, AI-assisted diagnostic technology was more effective in diagnosis.

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“…In addition, there is an increasing incidence of thyroid nodules in younger patients. Thyroid disease is 8 times more common in women compared to men (1)(2)(3). Thyroid lesions are mostly benign, presenting as nodular goiter and thyroid tumors.…”

Section: Introductionmentioning

confidence: 99%

Systematic review and meta-analysis: diagnostic value of different ultrasound for benign and malignant thyroid nodules

Wang¹,

Zhou²,

Shen³

et al. 2022

Gland Surg

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TNSNet: Thyroid nodule segmentation in ultrasound imaging using soft shape supervision

Cited by 23 publications

References 19 publications

A Multitask Approach for Automated Detection and Segmentation of Thyroid Nodules in Ultrasound Images

A Multitask Approach for Automated Detection and Segmentation of Thyroid Nodules in Ultrasound Images

Accuracy of Ultrasound Diagnosis of Thyroid Nodules Based on Artificial Intelligence-Assisted Diagnostic Technology: A Systematic Review and Meta-Analysis

Systematic review and meta-analysis: diagnostic value of different ultrasound for benign and malignant thyroid nodules

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